Visual Storytelling on Instagram: Redefining Digital Narratives in Indian Contexts
In contemporary India, Instagram has evolved into a powerful site of visual storytelling where images, reels, and aesthetic choices continually reshape how narratives are created, shared, and consumed. This paper explores how Indian creators—from rural artisans and independent journalists to lifestyle influencers and grassroots activists—use Instagram to express identity, culture, and socio-political consciousness. Moving beyond the platform’s commercialised image culture, the study highlights how Indian users adapt Instagram’s global features into localised storytelling traditions such as “darshana,” “kahani,” “dastangoi,” and “lok-drishti.” Through qualitative digital ethnography, content analysis of 200 Instagram accounts, and semi-structured interviews with 25 creators, this paper argues that Instagram in India is not merely a social media platform but a transformative narrative ecosystem. It enables micro-communities, democratizes content creation, and crafts new hybrid forms of cultural expression that blend imagery, captions, hashtags, and audio-visual cues. Findings reveal that Indian Instagram storytelling is shaped by regional languages, socio-economic aspirations, caste-class markers, digital literacy gaps, and platform algorithms that reward emotion-driven, relatable, and visually immersive content. The paper concludes with implications for digital culture, media literacy, and storytelling futures in India.
Published by: Vidhi Sikka, Mayank Arora
Author: Vidhi Sikka
Paper ID: V11I6-1190
Paper Status: published
Published: November 21, 2025
Interpreting Opaque Scheduling Heuristics in Timetabling via Surrogate GNN
Opaque timetabling heuristics often deliver strong schedules yet remain difficult to interpret, limiting trust, diagnosis, and controlled adaptation in educational scenarios. This work presents a practical pipeline that learns a constraint-aware graph neural network (GNN) surrogate from input-output pairs of a black-box timetabling solver and then applies global explainability to characterize the surrogate’s decision-making logic across entities and relations in the context of timetabling. Using a synthetic dataset with hard constraints, the study evaluates both the assignment fidelity to the solver outputs and the feasibility under hard constraints, complementing these with global explanations and counterfactual sensitivity analysis. The results highlight which entities and relations most influence the predicted assignments. The pipeline is intended as a methods-oriented contribution that standardizes data generation, surrogate training, and explanation estimators for timetabling, enabling reproducible assessment of interpretability alongside fidelity and feasibility without claiming domain deployment readiness.
Published by: Kashish Ukey, Nakul Badwaik, Labdhi Soni, Ojas Kamde, Smita Nirkhi
Author: Kashish Ukey
Paper ID: V11I6-1159
Paper Status: published
Published: November 19, 2025
Chess and Business: Strategic Thinking, Risk, and Cognitive Ability
This paper explores how the strategic plays and discipline that chess cultivates translate into strategic thinking, cognitive and leadership abilities, and risk perception in the real business world. Studies cited in this paper show that people who interact with this strategic gameplay regularly generally display higher cognitive and problem-solving abilities and are more adept at dealing with high-stakes situations in the business world. This paper also looks at differences in risk-taking tendencies between different genders. This paper analyses the overlap between chess strategies and abilities that develop from regular interaction with the game, and the critical thinking, and business strategies which likely develop from the game as well, to the point where even the elite credit their success to the game.
Published by: Jaisal Kohli
Author: Jaisal Kohli
Paper ID: V11I6-1144
Paper Status: published
Published: November 17, 2025
An Intelligent Matchmaking System for Peer-Assisted Learning in Educational Communities
This study presents an AI-driven intelligent matchmaking system that facilitates peer-assisted learning in educational communities by dynamically connecting students based on academic profiles, shared interests, and availability. The system addresses critical challenges in modern digital learning environments, including inefficient peer matching, a lack of personalization, and low student engagement. By leveraging machine learning algorithms, particularly TF-IDF vectorization and cosine similarity, the platform intelligently forms collaborative study groups that promote peer-to-peer knowledge exchange. The system integrates gamified learning features, including badges, points, and leaderboards, to sustain motivation, and incorporates real-time progress tracking through comprehensive analytics dashboards. Built using a microservice architecture with React.js frontend and FastAPI backend, the platform demonstrates superior scalability and modularity compared with traditional learning management systems. The testing results indicate the successful implementation of all core functionalities with 100% test case pass rates across the user management, matchmaking, resource sharing, and gamification modules. The system represents a significant advancement in creating student-centered, adaptive digital learning ecosystems that foster meaningful academic collaboration beyond conventional classroom boundaries.
Published by: Vinay Kumar Siddha, Poojitha Karuturi, Palli Kalyan Babu, Kona Karthik, Sneha Pradhan
Author: Vinay Kumar Siddha
Paper ID: V11I6-1169
Paper Status: published
Published: November 16, 2025
The Role of Mathematics in Sports Statistics: Analyzing Player Performance
Mathematics forms the backbone of modern sports analytics, enabling the transformation of raw performance data into structured, quantifiable insights that enhance both individual and team evaluation. This study examines how mathematical and statistical tools—including weighted averages, regression analysis, principal component analysis (PCA), z-score standardization, clustering methods, Monte Carlo simulations, and Expected Goals (xG/xA) models—collectively contribute to a sophisticated understanding of athlete performance. By applying these techniques, the research demonstrates how multidimensional player data can be normalized, compared, and interpreted to produce objective ratings and identify key performance indicators. Furthermore, the study highlights the predictive power of mathematical modelling in sports. Regression-based forecasting and probabilistic simulations allow analysts to estimate goal-scoring likelihood, evaluate tactical strategies, and anticipate match outcomes with greater accuracy. Dimensionality-reduction methods, such as PCA and factor analysis, streamline complex datasets into meaningful components, revealing hidden patterns and correlations within player behavior. Beyond individual assessments, mathematics also enhances broader strategic decision-making in areas such as player recruitment, opponent analysis, load management, and game planning. The findings emphasize that the integration of quantitative models not only improves analytical precision but also reduces subjective bias, resulting in a more transparent and reliable framework for performance evaluation. Overall, this research illustrates the essential role mathematics plays in shaping the contemporary sports analytics landscape, demonstrating how rigorous quantitative analysis contributes to improved tactical understanding, more informed coaching decisions, and data-driven competitive advantage.
Published by: Kashyap Jalali
Author: Kashyap Jalali
Paper ID: V11I6-1147
Paper Status: published
Published: November 15, 2025
Harnessing the Wind: Evaluating Technological Advancements, Policy Frameworks, and Environmental Strategies for Large-Scale Wind Energy Adoption in India
India’s transition to clean energy is crucial for achieving its goal of 500 GW of non-fossil fuel capacity by 2030 and mitigating its dependence on coal. This research examines how advancements in wind energy technology, supportive policy frameworks, and sustainable environmental practices can collectively drive large-scale wind energy adoption in India. It explores the evolution of turbine design and efficiency, the promise of offshore wind energy, and the integration of digitalization and energy storage systems that enhance reliability and cost-effectiveness. The study further analyzes India’s policy landscape—including Power Purchase Agreements (PPAs), Renewable Purchase Obligations (RPOs), and incentives under the National Action Plan on Climate Change—highlighting their role in promoting investor confidence and infrastructure development. Moreover, it discusses environmental sustainability through Environmental Impact Assessments (EIAs), community engagement, and wildlife protection strategies. By comparing global best practices from Denmark, Germany, and China, the research identifies pathways for India to overcome challenges such as grid integration, regulatory inconsistencies, and land acquisition barriers. Ultimately, this paper concludes that a synergistic approach—combining technological innovation, policy support, and environmental stewardship—can position India as a global leader in sustainable wind energy development.
Published by: Vivaan Dumir
Author: Vivaan Dumir
Paper ID: V11I6-1164
Paper Status: published
Published: November 15, 2025
